import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel





class     lowerCAmelCase__     (				pl.LightningModule			):


		'''simple docstring'''






		def __init__(       self     :     Optional[int]     ,  snake_case__     :     Any		)      ->	List[str]:
				super().__init__()
				_lowerCamelCase				   =  model
				_lowerCamelCase				   =  2
				_lowerCamelCase				   =  nn.Linear(self.model.config.hidden_size     ,  self.num_labels		)



		def   _snake_case       (       self     :     Optional[Any]		)      ->	Tuple:
				pass



def 	lowerCamelCase					(	UpperCamelCase     :				str			,       UpperCamelCase     :				str			,       UpperCamelCase     :				str							)   ->    Optional[Any]:
		# load longformer model from model identifier
		_lowerCamelCase				   =  LongformerModel.from_pretrained(UpperCamelCase							)
		_lowerCamelCase				   =  LightningModel(UpperCamelCase							)

		_lowerCamelCase				   =  torch.load(UpperCamelCase			,       map_location=torch.device('cpu'							)							)
		lightning_model.load_state_dict(ckpt['state_dict']							)

		# init longformer question answering model
		_lowerCamelCase				   =  LongformerForQuestionAnswering.from_pretrained(UpperCamelCase							)

		# transfer weights
		longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()							)
		longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()							)
		longformer_for_qa.eval()

		# save model
		longformer_for_qa.save_pretrained(UpperCamelCase							)

		print(F"""Conversion successful. Model saved under {pytorch_dump_folder_path}"""							)


if __name__ == "__main__":
				A						=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--longformer_model',
				    default=None,
				    type=str,
				    required=True,
				    help='model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.',
				)
				parser.add_argument(
				    '--longformer_question_answering_ckpt_path',
				    default=None,
				    type=str,
				    required=True,
				    help='Path the official PyTorch Lightning Checkpoint.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
				)
				A						=			parser.parse_args()
				convert_longformer_qa_checkpoint_to_pytorch(
				    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
				)
from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge





@total_ordering
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def __lt__(       self     :     List[Any]     ,  snake_case__     :     int		)      ->	Any:
				return self[-1] < other[-1]



		def __eq__(       self     :     Tuple     ,  snake_case__     :     Union[str, Any]		)      ->	Union[str, Any]:
				return self[-1] == other[-1]



def 	lowerCamelCase					(	UpperCamelCase     :				list							)   ->    list:
		_lowerCamelCase				   =  []
		# sort into stacks
		for element in collection:
				_lowerCamelCase				   =  Stack([element]							)
				_lowerCamelCase				   =  bisect_left(UpperCamelCase			,       UpperCamelCase							)
				if i != len(UpperCamelCase							):
						stacks[i].append(UpperCamelCase							)
				else:
						stacks.append(UpperCamelCase							)

    # use a heap-based merge to merge stack efficiently
		_lowerCamelCase				   =  merge(*(reversed(UpperCamelCase							) for stack in stacks)							)
		return collection


if __name__ == "__main__":
				A						=			input('Enter numbers separated by a comma:\n').strip()
				A						=			[int(item) for item in user_input.split(',')]
				print(patience_sort(unsorted))